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SCOPUS
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 문인규 | - |
| dc.contributor.author | Ongee Jeong | - |
| dc.date.accessioned | 2025-02-28T21:01:02Z | - |
| dc.date.available | 2025-02-28T21:01:02Z | - |
| dc.date.issued | 2025 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.11750/57969 | - |
| dc.identifier.uri | http://dgist.dcollection.net/common/orgView/200000841197 | - |
| dc.description | Deep Learning, Data Analysis, Cryptanalysis, Privacy-Preserving | - |
| dc.description.tableofcontents | Ⅰ. INTRODUCTION 1 1.1. Motivations and Objectives 1 1.2. Overview 5 1.3. Contributions and Outline 7 Ⅱ. DEEP LEARNING-BASED ENCRYPTED DATA ANALYSIS 9 2.1. Deep Learning-based Cryptanalysis on Optical Cryptographic Algorithm 9 2.1.1. Methodology 9 2.1.2. Experiments 16 2.2. Deep Learning-based Cryptanalysis on Block Ciphers 23 2.2.1. Methodology 23 2.2.2. Experiments 36 2.3. Deep Learning-based Cryptanalysis on Public-Key Cryptography 49 2.3.1. Methodology 49 2.3.2. Experiments 52 Ⅲ. DEEP LEARNING-BASED OBFUSCATED DATA ANALYSIS 64 3.1. Methodology 64 3.1.1. Poisson-Multinomial Distribution-based Photon Counting Imaging (PMD-PCI) 64 3.1.2. Deep Learning-based Privacy-Preserving Image Classification Scheme 65 3.2. Experiments 70 3.2.1. Dataset 70 3.2.2. Implementation Details 70 3.2.3. Evaluation Metric 71 3.2.4. Results 72 Ⅳ. CONCLUSION AND FUTURE WORK 81 4.1. Summary and Discussion 81 References 85 요 약 문 91 |
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| dc.format.extent | 92 | - |
| dc.language | eng | - |
| dc.publisher | DGIST | - |
| dc.title | Automated Analysis of Encrypted and Obfuscated Data based on Deep Neural Networks | - |
| dc.title.alternative | 딥러닝 기반의 암호화 및 난독화 된 데이터 자동 분석 | - |
| dc.type | Thesis | - |
| dc.identifier.doi | 10.22677/THESIS.200000841197 | - |
| dc.description.degree | Doctor | - |
| dc.contributor.department | Department of Robotics and Mechatronics Engineering | - |
| dc.identifier.bibliographicCitation | Ongee Jeong. (2025). Automated Analysis of Encrypted and Obfuscated Data based on Deep Neural Networks. doi: 10.22677/THESIS.200000841197 | - |
| dc.contributor.coadvisor | Goo-Rak Kwon | - |
| dc.date.awarded | 2025-02-01 | - |
| dc.publisher.location | Daegu | - |
| dc.description.database | dCollection | - |
| dc.citation | XT.RD 정65 202502 | - |
| dc.date.accepted | 2025-01-20 | - |
| dc.contributor.alternativeDepartment | 로봇및기계전자공학과 | - |
| dc.subject.keyword | Deep Learning, Data Analysis, Cryptanalysis, Privacy-Preserving | - |
| dc.contributor.affiliatedAuthor | Ongee Jeong | - |
| dc.contributor.affiliatedAuthor | Inkyu Moon | - |
| dc.contributor.affiliatedAuthor | Goo-Rak Kwon | - |
| dc.contributor.alternativeName | 정온지 | - |
| dc.contributor.alternativeName | Inkyu Moon | - |
| dc.contributor.alternativeName | 권구락 | - |
| dc.rights.embargoReleaseDate | 2030-02-28 | - |
